evaluate.py 17.8 KB
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#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Manuel Guenther <manuel.guenther@idiap.ch>
# Tue Jul 2 14:52:49 CEST 2013

from __future__ import print_function

"""This script evaluates the given score files and computes EER, HTER.
It also is able to plot CMC and ROC curves."""

import bob.measure

import argparse
import numpy, math
import os

# matplotlib stuff
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import matplotlib; matplotlib.use('pdf') #avoids TkInter threaded start
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from matplotlib import pyplot
from matplotlib.backends.backend_pdf import PdfPages

import bob.core
logger = bob.core.log.setup("bob.bio.base")


def command_line_arguments(command_line_parameters):
  """Parse the program options"""

  # set up command line parser
  parser = argparse.ArgumentParser(description=__doc__,
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)

  parser.add_argument('-d', '--dev-files', required=True, nargs='+', help = "A list of score files of the development set.")
  parser.add_argument('-e', '--eval-files', nargs='+', help = "A list of score files of the evaluation set; if given it must be the same number of files as the --dev-files.")

  parser.add_argument('-s', '--directory', default = '.', help = "A directory, where to find the --dev-files and the --eval-files")

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  parser.add_argument('-c', '--criterion', choices = ('EER', 'HTER', 'FAR'), help = "If given, the threshold of the development set will be computed with this criterion.")
  parser.add_argument('-f', '--far-value', type=float, default=0.1, help = "The FAR value in %% for which to evaluate (only for --criterion FAR)")
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  parser.add_argument('-x', '--cllr', action = 'store_true', help = "If given, Cllr and minCllr will be computed.")
  parser.add_argument('-m', '--mindcf', action = 'store_true', help = "If given, minDCF will be computed.")
  parser.add_argument('--cost', default=0.99,  help='Cost for FAR in minDCF')
  parser.add_argument('-r', '--rr', action = 'store_true', help = "If given, the Recognition Rate will be computed.")
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  parser.add_argument('-t', '--thresholds', type=float, nargs='+', help = "If given, the Recognition Rate will incorporate an Open Set handling, rejecting all scores that are below the given threshold; when multiple thresholds are given, they are applied in the same order as the --dev-files.")
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  parser.add_argument('-l', '--legends', nargs='+', help = "A list of legend strings used for ROC, CMC and DET plots; if given, must be the same number than --dev-files.")
  parser.add_argument('-F', '--legend-font-size', type=int, default=18, help = "Set the font size of the legends.")
  parser.add_argument('-P', '--legend-position', type=int, help = "Set the font size of the legends.")
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  parser.add_argument('-T', '--title', nargs = '+', help = "Overwrite the default title of the plot for development (and evaluation) set")
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  parser.add_argument('-R', '--roc', help = "If given, ROC curves will be plotted into the given pdf file.")
  parser.add_argument('-D', '--det', help = "If given, DET curves will be plotted into the given pdf file.")
  parser.add_argument('-C', '--cmc', help = "If given, CMC curves will be plotted into the given pdf file.")
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  parser.add_argument('-E', '--epc', help = "If given, EPC curves will be plotted into the given pdf file. For this plot --eval-files is mandatory.")
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  parser.add_argument('--parser', default = '4column', choices = ('4column', '5column'), help="The style of the resulting score files. The default fits to the usual output of score files.")
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  # add verbose option
  bob.core.log.add_command_line_option(parser)

  # parse arguments
  args = parser.parse_args(command_line_parameters)

  # set verbosity level
  bob.core.log.set_verbosity_level(logger, args.verbose)


  # some sanity checks:
  if args.eval_files is not None and len(args.dev_files) != len(args.eval_files):
    logger.error("The number of --dev-files (%d) and --eval-files (%d) are not identical", len(args.dev_files), len(args.eval_files))

  # update legends when they are not specified on command line
  if args.legends is None:
    args.legends = [f.replace('_', '-') for f in args.dev_files]
    logger.warn("Legends are not specified; using legends estimated from --dev-files: %s", args.legends)

  # check that the legends have the same length as the dev-files
  if len(args.dev_files) != len(args.legends):
    logger.error("The number of --dev-files (%d) and --legends (%d) are not identical", len(args.dev_files), len(args.legends))

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  if args.thresholds is not None:
    if len(args.thresholds) == 1:
      args.thresholds = args.thresholds * len(args.dev_files)
    elif len(args.thresholds) != len(args.dev_files):
      logger.error("If given, the number of --thresholds imust be either 1, or the same as --dev-files (%d), but it is %d", len(args.dev_files), len(args.thresholds))
  else:
    args.thresholds = [None] * len(args.dev_files)

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  if args.title is not None:
    if args.eval_files is None and len(args.title) != 1:
      logger.warning("Ignoring the title for the evaluation set, as no evaluation set is given")
    if args.eval_files is not None and len(args.title) < 2:
      logger.error("The title for the evaluation set is not specified")

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  return args


def _plot_roc(frrs, colors, labels, title, fontsize=18, position=None):
  if position is None: position = 4
  figure = pyplot.figure()
  # plot FAR and CAR for each algorithm
  for i in range(len(frrs)):
    pyplot.semilogx([100.0*f for f in frrs[i][0]], [100. - 100.0*f for f in frrs[i][1]], color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])

  # finalize plot
  pyplot.plot([0.1,0.1],[0,100], "--", color=(0.3,0.3,0.3))
  pyplot.axis([frrs[0][0][0]*100,100,0,100])
  pyplot.xticks((0.01, 0.1, 1, 10, 100), ('0.01', '0.1', '1', '10', '100'))
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  pyplot.xlabel('FAR (%)')
  pyplot.ylabel('CAR (%)')
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  pyplot.grid(True, color=(0.6,0.6,0.6))
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)
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  figure.set_tight_layout(True)
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  return figure


def _plot_det(dets, colors, labels, title, fontsize=18, position=None):
  if position is None: position = 1
  # open new page for current plot
  figure = pyplot.figure(figsize=(8.2,8))

  # plot the DET curves
  for i in range(len(dets)):
    pyplot.plot(dets[i][0], dets[i][1], color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])

  # change axes accordingly
  det_list = [0.0002, 0.001, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 0.95]
  ticks = [bob.measure.ppndf(d) for d in det_list]
  labels = [("%.5f" % (d*100)).rstrip('0').rstrip('.') for d in det_list]
  pyplot.xticks(ticks, labels)
  pyplot.yticks(ticks, labels)
  pyplot.axis((ticks[0], ticks[-1], ticks[0], ticks[-1]))

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  pyplot.xlabel('FAR (%)')
  pyplot.ylabel('FRR (%)')
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  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)
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  figure.set_tight_layout(True)
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  return figure

def _plot_cmc(cmcs, colors, labels, title, fontsize=18, position=None):
  if position is None: position = 4
  # open new page for current plot
  figure = pyplot.figure()

  max_x = 0
  # plot the DET curves
  for i in range(len(cmcs)):
    x = bob.measure.plot.cmc(cmcs[i], figure=figure, color=colors[i], lw=2, ms=10, mew=1.5, label=labels[i])
    max_x = max(x, max_x)

  # change axes accordingly
  ticks = [int(t) for t in pyplot.xticks()[0]]
  pyplot.xlabel('Rank')
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  pyplot.ylabel('Probability (%)')
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  pyplot.xticks(ticks, [str(t) for t in ticks])
  pyplot.axis([0, max_x, 0, 100])
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)
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  figure.set_tight_layout(True)
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  return figure
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def _plot_epc(scores_dev, scores_eval, colors, labels, title, fontsize=18, position=None):
  if position is None: position = 4
  # open new page for current plot
  figure = pyplot.figure()

  # plot the DET curves
  for i in range(len(scores_dev)):
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    bob.measure.plot.epc(scores_dev[i][0], scores_dev[i][1], scores_eval[i][0], scores_eval[i][1], 100, color=colors[i], label=labels[i], lw=2)
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  # change axes accordingly
  pyplot.xlabel('alpha')
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  pyplot.ylabel('HTER (%)')
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  pyplot.title(title)
  pyplot.grid(True)
  pyplot.legend(loc=position, prop = {'size':fontsize})
  pyplot.title(title)
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  figure.set_tight_layout(True)
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  return figure
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def main(command_line_parameters=None):
  """Reads score files, computes error measures and plots curves."""

  args = command_line_arguments(command_line_parameters)

  # get some colors for plotting
  cmap = pyplot.cm.get_cmap(name='hsv')
  colors = [cmap(i) for i in numpy.linspace(0, 1.0, len(args.dev_files)+1)]

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  if args.criterion or args.roc or args.det or args.epc or args.cllr or args.mindcf:
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    score_parser = {'4column' : bob.measure.load.split_four_column, '5column' : bob.measure.load.split_five_column}[args.parser]

    # First, read the score files
    logger.info("Loading %d score files of the development set", len(args.dev_files))
    scores_dev = [score_parser(os.path.join(args.directory, f)) for f in args.dev_files]

    if args.eval_files:
      logger.info("Loading %d score files of the evaluation set", len(args.eval_files))
      scores_eval = [score_parser(os.path.join(args.directory, f)) for f in args.eval_files]


    if args.criterion:
      logger.info("Computing %s on the development " % args.criterion + ("and HTER on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        # compute threshold on development set
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        if args.criterion == 'FAR':
          threshold = bob.measure.far_threshold(scores_dev[i][0], scores_dev[i][1], args.far_value/100.)
        else:
          threshold = {'EER': bob.measure.eer_threshold, 'HTER' : bob.measure.min_hter_threshold} [args.criterion](scores_dev[i][0], scores_dev[i][1])
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        # apply threshold to development set
        far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
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        if args.criterion == 'FAR':
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          print("The FRR at FAR=%2.3f%% of the development set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.far_value, args.legends[i], frr * 100., 100.*(1-frr)))
        else:
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          print("The %s of the development set of '%s' is %2.3f%%" % (args.criterion, args.legends[i], (far + frr) * 50.)) # / 2 * 100%
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        if args.eval_files:
          # apply threshold to evaluation set
          far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
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          if args.criterion == 'FAR':
            print("The FRR of the evaluation set of '%s' is %2.3f%% (CAR: %2.3f%%)" % (args.legends[i], frr * 100., 100.*(1-frr))) # / 2 * 100%
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          else:
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            print("The HTER of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (far + frr) * 50.)) # / 2 * 100%
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    if args.mindcf:
      logger.info("Computing minDCF on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        # compute threshold on development set
        threshold = bob.measure.min_weighted_error_rate_threshold(scores_dev[i][0], scores_dev[i][1], args.cost)
        # apply threshold to development set
        far, frr = bob.measure.farfrr(scores_dev[i][0], scores_dev[i][1], threshold)
        print("The minDCF of the development set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))
        if args.eval_files:
          # compute threshold on evaluation set
          threshold = bob.measure.min_weighted_error_rate_threshold(scores_eval[i][0], scores_eval[i][1], args.cost)
          # apply threshold to evaluation set
          far, frr = bob.measure.farfrr(scores_eval[i][0], scores_eval[i][1], threshold)
          print("The minDCF of the evaluation set of '%s' is %2.3f%%" % (args.legends[i], (args.cost * far + (1-args.cost) * frr) * 100. ))


    if args.cllr:
      logger.info("Computing Cllr and minCllr on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(scores_dev)):
        cllr = bob.measure.calibration.cllr(scores_dev[i][0], scores_dev[i][1])
        min_cllr = bob.measure.calibration.min_cllr(scores_dev[i][0], scores_dev[i][1])
        print("Calibration performance on development set of '%s' is Cllr %1.5f and minCllr %1.5f " % (args.legends[i], cllr, min_cllr))
        if args.eval_files:
          cllr = bob.measure.calibration.cllr(scores_eval[i][0], scores_eval[i][1])
          min_cllr = bob.measure.calibration.min_cllr(scores_eval[i][0], scores_eval[i][1])
          print("Calibration performance on evaluation set of '%s' is Cllr %1.5f and minCllr %1.5f" % (args.legends[i], cllr, min_cllr))


    if args.roc:
      logger.info("Computing CAR curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      fars = [math.pow(10., i * 0.25) for i in range(-16,0)] + [1.]
      frrs_dev = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_dev]
      if args.eval_files:
        frrs_eval = [bob.measure.roc_for_far(scores[0], scores[1], fars) for scores in scores_eval]

      logger.info("Plotting ROC curves to file '%s'", args.roc)
      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.roc)
        # create a separate figure for dev and eval
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        pdf.savefig(_plot_roc(frrs_dev, colors, args.legends, args.title[0] if args.title is not None else "ROC curve for development set", args.legend_font_size, args.legend_position))
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        del frrs_dev
        if args.eval_files:
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          pdf.savefig(_plot_roc(frrs_eval, colors, args.legends, args.title[1] if args.title is not None else "ROC curve for evaluation set", args.legend_font_size, args.legend_position))
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          del frrs_eval
        pdf.close()
      except RuntimeError as e:
        raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)

    if args.det:
      logger.info("Computing DET curves on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      dets_dev = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_dev]
      if args.eval_files:
        dets_eval = [bob.measure.det(scores[0], scores[1], 1000) for scores in scores_eval]

      logger.info("Plotting DET curves to file '%s'", args.det)
      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.det)
        # create a separate figure for dev and eval
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        pdf.savefig(_plot_det(dets_dev, colors, args.legends, args.title[0] if args.title is not None else "DET plot for development set", args.legend_font_size, args.legend_position))
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        del dets_dev
        if args.eval_files:
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          pdf.savefig(_plot_det(dets_eval, colors, args.legends, args.title[1] if args.title is not None else "DET plot for evaluation set", args.legend_font_size, args.legend_position))
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          del dets_eval
        pdf.close()
      except RuntimeError as e:
        raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)


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    if args.epc:
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      logger.info("Plotting EPC curves to file '%s'", args.epc)
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      if not args.eval_files:
        raise ValueError("To plot the EPC curve the evaluation scores are necessary. Please, set it with the --eval-files option.")
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      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.epc)
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        pdf.savefig(_plot_epc(scores_dev, scores_eval, colors, args.legends, args.title if args.title is not None else "EPC Curves" , args.legend_font_size, args.legend_position))
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        pdf.close()
      except RuntimeError as e:
        raise RuntimeError("During plotting of EPC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)




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  if args.cmc or args.rr:
    logger.info("Loading CMC data on the development " + ("and on the evaluation set" if args.eval_files else "set"))
    cmc_parser = {'4column' : bob.measure.load.cmc_four_column, '5column' : bob.measure.load.cmc_five_column}[args.parser]
    cmcs_dev = [cmc_parser(os.path.join(args.directory, f)) for f in args.dev_files]
    if args.eval_files:
      cmcs_eval = [cmc_parser(os.path.join(args.directory, f)) for f in args.eval_files]

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    if args.cmc:
      logger.info("Plotting CMC curves to file '%s'", args.cmc)
      try:
        # create a multi-page PDF for the ROC curve
        pdf = PdfPages(args.cmc)
        # create a separate figure for dev and eval
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        pdf.savefig(_plot_cmc(cmcs_dev, colors, args.legends, args.title[0] if args.title is not None else "CMC curve for development set", args.legend_font_size, args.legend_position))
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        if args.eval_files:
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          pdf.savefig(_plot_cmc(cmcs_eval, colors, args.legends, args.title[1] if args.title is not None else "CMC curve for evaluation set", args.legend_font_size, args.legend_position))
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        pdf.close()
      except RuntimeError as e:
        raise RuntimeError("During plotting of ROC curves, the following exception occured:\n%s\nUsually this happens when the label contains characters that LaTeX cannot parse." % e)

    if args.rr:
      logger.info("Computing recognition rate on the development " + ("and on the evaluation set" if args.eval_files else "set"))
      for i in range(len(cmcs_dev)):
        rr = bob.measure.recognition_rate(cmcs_dev[i], args.thresholds[i])
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        print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))
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        if args.eval_files:
          rr = bob.measure.recognition_rate(cmcs_eval[i], args.thresholds[i])
          print("The Recognition Rate of the development set of '%s' is %2.3f%%" % (args.legends[i], rr * 100.))